Process control systems, like those used in chemical, petroleum or other processes, typically include one or more process controllers and input/output (I/O) devices communicatively coupled to at least one host or operator workstation and to one or more field devices via analog, digital or combined analog/digital buses. The field devices, which may be, for example, valves, valve positioners, switches and transmitters (e.g., temperature, pressure and flow rate sensors), perform process control functions within the process such as opening or closing valves and measuring process control parameters. The process controllers receive signals indicative of process measurements made by the field devices, process this information to implement a control routine, and generate control signals that are sent over the buses or other communication lines to the field devices to control the operation of the process. In this manner, the process controllers may execute and coordinate control strategies using the field devices via the buses and/or other communication links.
Process information from the field devices and the controllers may be made available to one or more applications (i.e., software routines, programs, etc.) executed by the operator workstation (e.g., a processor-based system) to enable an operator to perform desired functions with respect to the process, such as viewing the current state of the process (e.g., via a graphical user interface), evaluating the process, modifying the operation of the process (e.g., via a visual object diagram), etc. Many process control systems also include one or more application stations (e.g., workstations) which are typically implemented using a personal computer, laptop, or the like and which are communicatively coupled to the controllers, operator workstations, and other systems within the process control system via a local area network (LAN). Each application station may include a graphical user interface that displays the process control information including values of process variables, values of quality parameters associated with the process, process fault detection information, and/or process status information.
Typically, displaying process information in the graphical user interface is limited to the display of a value of each process variable associated with the process. In some cases, methods of performing data analytics on the collected data are used to analyze process operation. For example, some process control systems may characterize simple relationships between some process variables to determine quality metrics associated with the process. While, in some cases, process data analytics can be quite complicated, such process data analytics have, for the most part, been performed off-line, i.e., after the process has been completed. While off-line data analytics are powerful tools used for many years by customers to determine, for example, root causes for quality issues for products, in cases where a resultant product of the process does not conform to predefined quality control metrics, the process and/other process variables are only analyzed after the completion of a batch, a process, and/or an assembly of the resulting product. While viewing the process and/or quality variables upon the completion of the process enables improvements to be implemented to the manufacturing or the processing of subsequent products, these improvements are not able to remediate the current completed products, which are out-of-specification.
This problem is particularly acute in batch processes, that is, in process control systems that implement batch processes. As is known, batch processes typically operate to process a common set of raw materials together as a “batch” through various numbers of stages or steps (which may defined by one or more stage, phases, or procedures), to produce a product. Multiple stages or steps of a batch process may be performed in the same equipment, such as in a tank, while others of the stages or steps may be performed in other equipment. Because the same raw materials are being processed differently over time in the different stages or steps of the batch process, in many cases within a common piece of equipment, it is difficult to accurately determine, during any stage or step of the batch process, whether the material within the batch is being processed in a manner that will likely result in the production of the end product that has desired or sufficient quality metrics. That is, because the temperature, pressure, consistency, pH, or other parameters of the materials being processed changes over time during the operation of the batch, many times while the material remains in the same location, it is difficult to determine whether the batch processes is operating at any particular time during the batch run in a manner that is likely to produce an end product with the desired quality metrics.
Thus, it is desirable in many instances to be able to perform analytical calculations on-line while a product is being manufactured as opposed to off-line (after a product is complete). On-line and off-line data analytics may use the same calculations, but on-line analytics allow the opportunity for taking corrective action before the product is complete.
One on-line analytical method of determining whether a currently operating batch is progressing normally or within desired specifications (and is thus likely to result in a final product having desired quality metrics) compares various process variable measurements made during the operation of the on-going batch with similar measurements taken during the operation of an exemplary or “golden batch.” In this case, a golden batch is a predetermined, previously run batch selected as a batch run that represents the normal or expected operation of the batch and that results in an end product with desired quality metrics. However, batch runs of a process typically vary in temporal length, i.e., vary in the time that it takes to complete the batch, making it difficult to know which time within the golden batch is most applicable to the currently measured parameters of an on-going batch being compared to the golden batch. Moreover, in many cases, batch process variables can vary widely during the batch operation, as compared to those of a selected golden batch, without a significant degradation in quality of the final product, meaning that the ongoing batch may still be operating properly even if process variables differ from the similar variables of the golden batch. Also, it is often difficult, if not practically impossible, to identify a particular batch run that is capable of being used in all cases as the golden batch to which all other batch runs should be compared.
A method of analyzing the results of on-going batch processes that overcomes some of the problems of using a golden batch involves creating a statistical model for the batch and using this statistical model to perform on-line analytics. This technique involves collecting data for each of a set of process variables (batch parameters) from a number of different batch runs of a batch process and identifying or measuring quality metrics for each of those batch runs. Thereafter, the collected batch parameters and quality data are used to create a statistical model of the batch, with the statistical model representing the “normal” operation of the batch that results in desired quality metrics. This statistical model of the batch can then be used on-line during process operation to analyze how different process variable measurements made during a particular batch run statistically relate to the same measurements within the batch runs used to develop the model. For example, this statistical model may be used to provide an average or a median value of each measured process variable, and a standard deviation associated with each measured process variable at any particular time during the batch run to which the currently measured process variables can be compared. Moreover, this statistical model may be used to predict how the current state of the batch will affect or relate to the ultimate quality of the batch product produced at the end of the batch.
Generally speaking, this type of batch modeling requires large amounts of data to be collected from various on-line sources such as transmitters, control loops, analyzers, virtual sensors, calculation blocks and manual entries. Most of the data for previously run batches is stored in continuous data historians. However, significant amounts of data and, in particular, manual entries, are usually associated with process management systems. Data extraction from both of these types of systems must be merged to satisfy model building requirements. Moreover, as noted above, a batch process normally undergoes several significantly different stages, steps or phases, from a technology and modeling standpoint. Therefore, a batch process is typically sub-divided with respect to the phases, and a model may be constructed for each phase. In this case, data for the same phase or stage or procedure, from many batch runs, is grouped to develop the statistical model for that phase or stage or procedure. The purpose of such a data arrangement is to remove or alleviate process non-linearities. Another reason to develop separate batch models on a stage basis, a phase basis, a procedure basis, or other basis is that, at various different stages of a batch, different process parameters are active and are used for modeling. As a result, a stage model can be constructed with a specific set of parameters relevant for each particular stage to accommodate or take into account only the process parameters relevant at each batch stage.
Various methods for performing statistically based, on-line data analytics within batch and continuous processes are described in more detail in U.S. Patent Application Publication Nos. 2010/0318934, 2011/0288660, 2011/0288837 and 2013/0069792, which generally describe methodologies for creating and executing on-line process models that enable process variable and process quality estimation, prediction and control. The disclosure of each of U.S. Patent Application Publication Nos. 2010/0318934, 2011/0288660, 2011/0288837 and 2013/0069792 is hereby expressly incorporated by reference herein. Generally speaking, the data analytical models and user interface methods described in these publications can be used to perform on-line and off-line process analysis and may be used to perform on-line process control while a process is executing, to thereby increase the quality of the products being produced by a process as the process is running.
Typically, to provide on-line data analytics, data from the various areas, regions, units, equipment, etc. of the plant must be collected for performing the analytics for each stage or part of a process being modeled during operation of the process being analyzed. The collection of this data may, in many cases, require the collection and processing of data that was not set up to be collected by the controller(s), field devices, batch executives, or other devices or modules within the process in the first place. Thus, in many cases, the addition of on-line data analytics requires an operator or a process configuration engineer to reconfigure the process control system by, for example, modifying the process to incorporate, generate or collect the new variables that are required as inputs for the data analytic models or calculations. For plant operators with a “locked down” or certified control system, making this modification presents a problem as, to do so, the plant operator needs to introduce the control system configuration changes and then re-certify and lock down the system again. This recertification process can be very expensive and time consuming.